A Hybrid Approach for COVID-19 Detection Using Biogeography-Based Optimization and Deep Learning

作     者：K.Venkatachalam Siuly Siuly M.Vinoth Kumar Praveen Lalwani Manas Kumar Mishra Enamul Kabir 

作者机构：Department of Computer Science and EngineeringCHRIST(Deemed to be University)Bangalore560074India Institute for Sustainable Industries&Liveable CitiesVictoria UniversityMelbourne14428Australia Department of Computer Science and EngineeringAnna UniversityUniversity College of EngineeringDindigul624622India School of SciencesUniversity of Southern QueenslandToowoombaDarling Heights4350Australia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第70卷第2期

页      面：3717-3732页

核心收录：

学科分类：0711[理学-系统科学] 07[理学] 08[工学] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 071102[理学-系统分析与集成] 081103[工学-系统工程] 

主　　题：Covid-19 biogeography-based optimization deep learning convolutional neural network computer vision 

摘      要：The COVID-19 pandemic has created a major challenge for countries all over the world and has placed tremendous pressure on their public health care *** early diagnosis of COVID-19 may reduce the impact of the *** achieve this objective,modern computation methods,such as deep learning,may be *** this study,a computational model involving deep learning and biogeography-based optimization(BBO)for early detection and management of COVID-19 is ***,BBO is used for the layer selection process in the proposed convolutional neural network(CNN).The computational model accepts images,such as CT scans,X-rays,positron emission tomography,lung ultrasound,and magnetic resonance imaging,as *** the comparative analysis,the proposed deep learning model CNNis compared with other existingmodels,namely,VGG16,InceptionV3,ResNet50,and *** the fitness function formation,classification accuracy is considered to enhance the prediction capability of the proposed *** results demonstrate that the proposed model outperforms InceptionV3 and ResNet50.